However, graphs are inherently unordered, irregular, and combinatorial
in nature made up of nodes / vertices and edges / links
between nodes, while most ML methods (e.g. neural networks) favor
continuous vector representations. To get around the difficulties in
using discrete graph representations in ML, graph embedding methods
learn a continuous vector space for the graph, assigning each node
(and/or edge) in the graph to a specific position in a vector space. We
refer the reader to a survey on node embeddings [1]} for more
details.
